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Related Experiment Video

Updated: Jul 29, 2025

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DenseNet_ HybWWoA: A DenseNet-Based Brain Metastasis Classification with a Hybrid Metaheuristic Feature Selection

Abdulaziz Alshammari1

  • 1Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.

Biomedicines
|May 27, 2023
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Summary
This summary is machine-generated.

This study introduces a new AI method for brain tumor classification using the Hybrid Whale and Water Waves Optimization Algorithm (HybWWoA) and DenseNet. The novel approach achieves high accuracy in identifying brain metastases, improving diagnostic capabilities.

Keywords:
MRIbrain metastasesclassificationfeature selectionneural networkoptimization

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Brain metastases (BM) are a severe complication of cancer, often originating from lung, breast, or melanoma.
  • Current diagnostic and treatment options for BM have limitations, highlighting the need for improved methods.
  • Magnetic Resonance Imaging (MRI) is crucial for detecting brain tumors but faces challenges with specificity.

Purpose of the Study:

  • To develop a novel method for categorizing brain tumors, specifically brain metastases.
  • To enhance the accuracy and efficiency of brain tumor identification using advanced computational techniques.

Main Methods:

  • A hybrid optimization algorithm, the Hybrid Whale and Water Waves Optimization Algorithm (HybWWoA), was developed to reduce feature dimensions.
  • The DenseNet algorithm was employed for the final brain tumor categorization.
  • The HybWWoA algorithm integrates whale optimization and water waves optimization principles.

Main Results:

  • The proposed method demonstrated high performance in classifying brain tumors.
  • Achieved an F1-score of 97%, with accuracy, precision, and recall of 92.1%, 98.5%, and 92.1%, respectively.
  • The algorithm effectively reduced feature size, aiding in precise tumor categorization.

Conclusions:

  • The novel AI-driven approach significantly improves brain tumor classification accuracy.
  • This method offers a promising advancement in the diagnostic capabilities for brain metastases.
  • The Hybrid Whale and Water Waves Optimization Algorithm combined with DenseNet shows superior performance in medical image analysis.